Microfinance investment in Sub-Saharan Africa : turning opportunities into reality
Bibliographic record
Abstract
Yet despite healthy economic prospects, the region has the lowest share of banked households in the world (12 percent) and the highest share of poor people, with 50 percent of the population living on $1.25 a day or less (Consultative Group to Assist the Poor, or CGAP and World Bank 2010). More work needs to be done to expand financial access, and many governments and international funders are keen to contribute. Equity and debt capital continues to be important in developing financial services for low-income populations in the region. However, local equity is not available in most countries, and local debt funding is scarce. Sub-Saharan Africa (SSA) microfinance relies heavily on deposit funding, mostly composed of short-term deposits, while many smaller institutions cannot attract sufficient deposits to finance growth. The region received 11 percent of global microfinance funding commitments in 2010.4 In terms of cross-border investment, it received among the lowest levels in the world, $1 billion out of a total of $13 billion as of December 2010 (Reille, Forster, and Rozas 2011). This brief examines public and private foreign investment in SSA microfinance retailers, and the key challenges that limit investment. The findings are based on CGAP data on cross-border funding flows, publicly available resources, and interviews with more than 30 investors and other stakeholders conducted in the first quarter of 2012.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".